TextCLIP: Text-Guided Face Image Generation And Manipulation Without Adversarial Training

• URL: http://arxiv.org/abs/2309.11923v1
• Date: Thu, 21 Sep 2023 09:34:20 GMT
• Title: TextCLIP: Text-Guided Face Image Generation And Manipulation Without Adversarial Training
• Authors: Xiaozhou You, Jian Zhang
• Abstract summary: We propose TextCLIP, a unified framework for text-guided image generation and manipulation without adversarial training. Our proposed method outperforms existing state-of-the-art methods, both on text-guided generation tasks and manipulation tasks.
• Score: 5.239585892767183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-guided image generation aimed to generate desired images conditioned on given texts, while text-guided image manipulation refers to semantically edit parts of a given image based on specified texts. For these two similar tasks, the key point is to ensure image fidelity as well as semantic consistency. Many previous approaches require complex multi-stage generation and adversarial training, while struggling to provide a unified framework for both tasks. In this work, we propose TextCLIP, a unified framework for text-guided image generation and manipulation without adversarial training. The proposed method accepts input from images or random noise corresponding to these two different tasks, and under the condition of the specific texts, a carefully designed mapping network that exploits the powerful generative capabilities of StyleGAN and the text image representation capabilities of Contrastive Language-Image Pre-training (CLIP) generates images of up to $1024\times1024$ resolution that can currently be generated. Extensive experiments on the Multi-modal CelebA-HQ dataset have demonstrated that our proposed method outperforms existing state-of-the-art methods, both on text-guided generation tasks and manipulation tasks.

Related papers

• Visual Text Generation in the Wild [67.37458807253064]
  We propose a visual text generator (termed SceneVTG) which can produce high-quality text images in the wild. The proposed SceneVTG significantly outperforms traditional rendering-based methods and recent diffusion-based methods in terms of fidelity and reasonability. The generated images provide superior utility for tasks involving text detection and text recognition.
  arXiv  Detail & Related papers  (2024-07-19T09:08:20Z)
• Text-based Person Search without Parallel Image-Text Data [52.63433741872629]
  Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description. Existing methods are dominated by training models with parallel image-text pairs, which are very costly to collect. In this paper, we make the first attempt to explore TBPS without parallel image-text data.
  arXiv  Detail & Related papers  (2023-05-22T12:13:08Z)
• Variational Distribution Learning for Unsupervised Text-to-Image Generation [42.3246826401366]
  We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training. We employ a pretrained CLIP model, which is capable of properly aligning embeddings of images and corresponding texts in a joint space. We optimize a text-to-image generation model by maximizing the data log-likelihood conditioned on pairs of image-text CLIP embeddings.
  arXiv  Detail & Related papers  (2023-03-28T16:18:56Z)
• Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation [63.061871048769596]
  We present a novel Unified Multi-Modal Latent Diffusion (UMM-Diffusion) which takes joint texts and images containing specified subjects as input sequences. To be more specific, both input texts and images are encoded into one unified multi-modal latent space. Our method is able to generate high-quality images with complex semantics from both aspects of input texts and images.
  arXiv  Detail & Related papers  (2023-03-16T13:50:20Z)
• StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training [64.37272287179661]
  StrucTexTv2 is an effective document image pre-training framework. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling. It achieves competitive or even new state-of-the-art performance in various downstream tasks such as image classification, layout analysis, table structure recognition, document OCR, and information extraction.
  arXiv  Detail & Related papers  (2023-03-01T07:32:51Z)
• ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation [22.47279425592133]
  We propose ERNIE-ViLG, a unified generative pre-training framework for bidirectional image-text generation. For the text-to-image generation process, we propose an end-to-end training method to jointly learn the visual sequence generator and the image reconstructor. We train a 10-billion parameter ERNIE-ViLG model on a large-scale dataset of 145 million (Chinese) image-text pairs.
  arXiv  Detail & Related papers  (2021-12-31T03:53:33Z)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)
• Towards Open-World Text-Guided Face Image Generation and Manipulation [52.83401421019309]
  We propose a unified framework for both face image generation and manipulation. Our method supports open-world scenarios, including both image and text, without any re-training, fine-tuning, or post-processing.
  arXiv  Detail & Related papers  (2021-04-18T16:56:07Z)
• Text to Image Generation with Semantic-Spatial Aware GAN [41.73685713621705]
  A text to image generation (T2I) model aims to generate photo-realistic images which are semantically consistent with the text descriptions. We propose a novel framework Semantic-Spatial Aware GAN, which is trained in an end-to-end fashion so that the text encoder can exploit better text information.
  arXiv  Detail & Related papers  (2021-04-01T15:48:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.